Hi! I’m Adrian.
Data scientist
& developer.

I ♥ data

Data is an exact thumbprint of the world's behaviour and psychology at
any given moment.

With data, I get to know what the world is thinking and feeling and
what it will do next.

Things I've done

AWS film shoot

I talked to Amazon Web Services about Carl, one of the awesome machine
learning tools that we built at Airtasker.

Carl is an algorithm that classifies the tasks posted to the Airatasker
platform. He’s named after Carl Linnaeus.

Google Street View API

Google’s API doesn’t allow you to download old images from Street View.
So, I made a Python library to download old and new photos from Google
Steet View.

Babies and cats

I used some fancy maths and Google trends to uncover relationships between
people's online searches and their economy. I found out that U.S. states
with higher rates of infant mortality Google for bad credit, loans, and
information on sexually transmitted diseases.

Google data API

Robot races

I teach kids a crash course in programming Lego robots. Classes are for
school years 6+. At the end, the kids race their robots against the clock
through an obstical course using a combination of light and ultrasonic
sensors.

Christmas lecture

I co-hosted a 2015 Christmas Lecture at the University of Warwick. The show
was about robotics, and all the different parts that make a functioning
robot, as well as the roles these robots now play in society.

Where I've spent my time

Airtasker helps you to realise the full value of your skills.

Senior Data Scientist

May 2017 - Present

I’m responsible for building the machine learning pipeline and
algorithms at Airtasker. I've built systems to:

- Automate platform moderation. Many things posted to the platform
do not fit with our community guidelines. We have a set of
algorithms as our front-line defense removing undesirable
content.

- Categorise tasks. Hundreds of thousands of tasks are posted to
Airtasker. We automatically classify the to help our users find
what they are looking for.

- Recommendation systems. Due to the shear volume of tasks on the
platform, we use recommendation algorithms to help surface the
right tasks to our users.

- Pricing algorithms. Understanding how much a task is worth is
important for both sides of the transaction.

- Productionising machine learning algorithms. Developing an
algorithm is only a small fraction of delivering a machine learning
solution. Most of the battle is productionising the models in such
a way that we can rely on them, monitor them, and push out updates
quickly.

When you’re learning maths, Mathspace is the right help at the right
time.

R&D Software Engineer & Data Scientist

September 2016 - May 2017 (9 months)

I built tools to help Mathspace understand their data and create
more value for their customers and the company. I developed the next
generation of adaptive learning algorithms to further automate the
learning experience. Kids are now learning independently.

Data Scientist

May 2014 - June 2016 (2 years 2 months)

I used online data such as Google search logs to predict real world
behaviour like future stock prices. Highlights include:

- Engineered software on a Python/Javascript/HTML/CSS stack to
predict future student numbers of the Warwick Business school and
improve business operations.

- Engineered various web tools in for business stakeholders to
analyse large datasets.-Lead the development or had a hand in
developing any software that was used outside of our team.

- Created a multitude of data visualisations and infographics to
showcase my own work and the work of the team.

Ph.D. Student

2011 - 2013 (3 years)

I developed a rigorous mathematical framework for traditional
methods of trading. I used statistical analysis to dissect and
understand how the methods generated profits.